Identity vs. Identifier vs. Identity Graph: What’s the Difference?

Identity vs. Identifier vs. Identity Graph: What’s the Difference?

Marketers are grappling with a lot of meaty topics nowadays—none more so than the concept of “identity.” It’s a difficult and important topic because identity is uniquely tied to so many meaty topics – customer data, privacy, digital experience, and measurement. “Identity” raises existential questions, both theoretical and practical, for marketers. Answering these questions goes right to the foundational decisions every modern brand must make.

First things first: What do we mean by “identity”?

In the context of marketing and customer data, there are two terms that are often used interchangeably, but shouldn’t be confused with one another:

Identity is conceptually simple. Identity refers to an individual person, and it can evolve and change over time, much like the person herself. For example, though you remain the same human being, your identity as a student is replaced by employee when you enter the workforce. Your identity as a value-shopper may change if you get a big raise. The fact that you are one, distinct person never changes. Attributes that make up your distinct self, though, absolutely do. Thus, identity is fluid and ever-changing, but inextricably linked to a single person.

An identifier, on the other hand, is a piece of information that helps you recognize a distinct person. Depending on the context, an identifier might be an anonymous cookie ID, a device ID, an email address, or a customer record. Each of these can be useful in their way, but they are decidedly not all created equal in terms of marketing utility, and certainly not when it comes to privacy.

Using identifiers, marketing technologies like CDPs give you a mechanism to create ways to maintain identity within a system:

The profile: With profiles, marketers gain a mechanism to maintain a single repository of information about an individual person. Different kinds of systems support different profile types and structures – each with its own strengths and weaknesses when it comes to marketing utility. For example, a few profile flavors:

Pre-defined attributes, where the system determines what types of data it will store in the profile

Extensible data storage profiles, where the marketer can choose what data and in what format the data is stored – structured or unstructured data, text or whole number, synthetic or calculated profiles

Most brands have dozens of profiles associated with a single person, because of fragmented and siloed data management. But the quest for a single customer view, which sparked the meteoric rise of customer data platforms, led marketers to reconcile all disparate profiles into one profile that represents an individual person and their identity.

The identity graph: Often used interchangeably (and incorrectly), a profile and an identity graph are similar in that both are constructs of customer data, aggregated from disparate systems, channels, and sources. While an identity graph is a moment-in-time snapshot of the current, raw state of your customer data – this snapshot isn’t stored beyond that moment-in-time. With an identity graph, the data itself continues to reside in separate systems and can be queried to create a current view of the data, including to account for any changes.

In other words, while a profile allows you to resolve multiple identities into a single, consolidated identity, the identity graph allows you to associate, but not consolidate, multiple identities with one master ID.

For first-party data to be unified and actionable across not only disparate systems, but also for different marketing programs (advertising, direct marketing, analytics, etc.), the best home for a single source of identity truth is a profile, which can be readily utilized by an identity graph for certain use cases.

What’s the role of identity in marketing today?

The rise of digital channels where consumers engage with brands – on websites, mobile apps, social platforms, etc. – drove the creation of solutions where marketers can recognize people by all their different proxies. Then, marketers’ wanted to stitch all of the proxies together into one because it improves business results. Identity is an essential pillar of marketing today. (Even television, the most mass media of all, isn’t immune, between addressable TV and OTT.) Identity plays a variety of roles within marketing, contingent upon:

The type of identifier used to distinguish one person from another. A cookie ID and an email address are fundamentally different identifiers, both functionally and in terms of privacy and value to the marketer.

Where the data is collected and how it will be used. First-party data and third-party data both use identity but cannot always be used in tandem (e.g. DMPs have to use pseudonymous identifiers and anonymization techniques to prevent co-mingling data collected for different purposes). Compliance with data collection laws and regulations such as GDPR also influence this.

Matching techniques and use cases. Fuzzy or probabilistic matching is, by definition, an imperfect science. Match rates between one dataset and another vary widely and can be difficult to test if the matching dataset is a third-party black box. Deterministic matches are more precise but happen less frequently because of a higher threshold for the data. Each has its purpose but should be considered in the context of how you will use the data. You can tolerate less precision for a massive acquisition ad campaign; imprecision would spoil a campaign that targets a unique attribute of a small set of individuals with a particularly high value and specific set of traits.

How does your brand identify (get it?) the right identity solution?

At RampUp, a panel discussed “Three Flavors of Identity Solutions.” Do I go with a CDP, an onboarding solution, or a graph (i.e. an indentity or device graph)? It depends on what you’re trying to do, but here’s a good rule of thumb:

Onboardingsolution – Onboarding is most effective for improving advertising campaigns by starting with first-party data and matching based on attributes available in paid channels.

Customer data platform – With persistent profiles, CDPs unify all first-party customer data and associated identifiers, which you can then activate in a variety of marketing applications – for measurement, campaign orchestration, personalization, etc.

Still have questions? Want to talk through your specific situation? Let’s talk.

Cory has spent her career on the cutting edge of marketing technology and brings years working with Fortune 500 clients from various industries to BlueConic. Before joining the BlueCrew, she was an analyst at Forrester Research where she covered business and consumer technology trends and the fast-moving marketing tech landscape. A sought-after speaker and industry voice, Cory’s work has been featured in Venture Beat, Wired, AdAge, and AdWeek, as well as spoken at conferences such as FutureM, MITX, and the Association of National Advertisers.